A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

被引：6

作者：

Wu, Chin-Chia ^{[1
]}

Azzouz, Ameni ^{[2
]}

Chen, Jia-Yang ^{[1
]}

Xu, Jianyou ^{[3
]}

Shen, Wei-Lun ^{[1
]}

Lu, Lingfa ^{[4
]}

Ben Said, Lamjed ^{[2
]}

Lin, Win-Chin ^{[1
]}

机构：

[1] Feng Chia Univ, Dept Stat, 100 Wenhwa Rd, Taichung 40724, Taiwan

[2] Univ Tunis, Insitut Super Gest Tunis, MART Lab, Tunis 2000, Tunisia

[3] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China

[4] Zhengzhou Univ, Sch Math & Stat, Zhengzhou 450001, Henan, Peoples R China

来源：

COMPLEX & INTELLIGENT SYSTEMS | 2022年 / 8卷 / 01期

关键词：

Cloud simulated annealing algorithm; Genetic algorithm; Multitasking; Scheduling; Simulated annealing algorithm; Two agent; DUE-DATE ASSIGNMENT; 2 COMPETING AGENTS; SINGLE-MACHINE; TIME; OPTIMIZATION; ALGORITHM; OPERATIONS; MINIMIZE; MODEL;

D O I：

10.1007/s40747-021-00355-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

引用

页码：199 / 212

页数：14

共 59 条

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